Instigated by the great success of dynamic matrix control (DMC), the theory of model predictive control (MPC) has been receiving intensive attention in the process control area. The basic idea of MPC is to use a model to predict the future output trajectory of a process and compute a series of controller actions to minimize the difference between the predicted trajectory and a user-specified one, subject to constraints. It is clear that MPC demands a dynamic process model of proper accuracy and execution speed, though the feedback mechanism of MPC tolerates some model mismatch. Artificial neural networks (ANNs) as a process model for control purpose are recognized superior to other conventional modeling methods. Artificial neural networks provide a general approach for extracting process dynamics from input-output data only. Their learning ability makes them versatile and friendly for practical applications. With their great power for approximating complex functionality, their compact form and great speed of information retrieval make them highly suitable for online uses. Because of their empirical characteristics, ANN models need to be trained with a lot of operation data to cover certain operating ranges of process. Uncertainty of an ANN model often exists and may be severe for some special ranges. For instance, in a range around the equivalence point of neutralization where the process output (pH) is highly sensitive to the manipulated variable (flow rate of acid or base stream), training data is usually scarce, and an ANN model would be hence very rough.